Up to now, many systems for character recognition have been proposed and commercialized, utilizing two basic principles, one of which is a structural analysis and the other is at a pattern matching. As for the former, generally the system for recognition is not heavy, therefore it can be applied to a case where input restriction is strong, specifically an object where the number of strokes and/or the stroke order are constant. On the other hand, the latter is applied to both cases, a case in which the number of strokes and the stroke order are free or a case near to that.
A method utilizing structural analysis is disclosed in the “Online real-time recognition for handwritten number/katakana character”, Transaction of Institute of Electronics, Information and Communication Engineers, 56-D, 5, PP. 312-319, and Japanese Unexamined Patent Publication No. S59-131972 issued by Japan patent Office, in which a so-called basic stroke system is described. The stroke is classified to a simple stroke (four kinds) and a complex stroke (seven kinds) and is recognized by a discriminating automaton, and consequently though it is simple, there are problems creating a dictionary or dealing with continuous characters and simplified characters, and also there is a problem for the possibility.
The pattern matching method includes two kinds of methods roughly separately. As disclosed in “Online recognition for handwritten character by a point approximation for stroke” in Transaction of Institute of Electronics, Information and Communication Engineers, J63-D, 2, PP. 153-160, one method is that the stroke is approximated with a few points and these are made to be feature points. Also the moving directions of the stroke at end point are estimated and those are also made to be feature points, and feature vectors are formed. Dictionaries are disassembled to strokes having feature vectors, and the correspondence of input vectors and the feature vectors prepared for each category is taken. And, as for the dictionaries in which the correspondence was taken, the distance is calculated, and a name of the dictionary that gives a minimum distance is a character's name, and basically it is free to the stroke order or number of strokes.
There is another pattern matching method, which is described in “Hand written character recognition by Rubber String Matching Method” disclosed in a technical paper PRL74-20 of Technical Committee on Electronics and Communications as original paper of the corresponding system to feature points, and also as disclosed in Japanese Unexamined Patent Publications No. S57-45679 and No. H8-24942 issued by Japan patent Office. This method involves taking the correspondence of the feature point vectors of input character and dictionary by a DP (Dynamic Programming) method, which is used as the main stream in the online recognition of the handwritten character.
It should be noted that offline character recognition technology has been applied to online character recognition technology recently.
Specifically, there is OCR technology that has been accumulated up to now, and this is able to be used for the online character recognition.
From this standpoint, when seeing the OCR technology, the mainstream is a directional feature matching technology. There are a large number of papers about such technology, but the fundamental method exists in one improvement of correlative method”, Transaction of Institute of Electronics, Information and Communication Engineers, J62-D, 3, PP. 217-224, for example. The fundamental difference between this method and the method of structural analysis, is that the feature is generally assigned to a grid plane of n×m, and the feature distribution on this plane is made to be an representation of the final input character, and the (n×m) is scanned to the right from the left corner, for example, and is made to be vector of n×m dimensions. The discrimination is that the inner product (similarity) between a standard directional feature vector and an input character directional vector is calculated and a category's name of a standard directional feature vector with the highest value is made to be an answer. In this case, especially, to a handwritten character with remarkable transformation, the preprocessing of advanced nonlinear normalization is performed. The reason that this is needed is because this method makes the grid plane of n×m a basic framework. An advantage of this method is generally that an advanced theory of discrimination may be used, because a vector space, specifically Hilbert spatial theory, is applied. Also, it is strong for the noise if it says practicably. However, it may kill the biggest advantage of on-line usage, specifically the easiness of segmentation. In the off-line situation, for example, even if many pieces of characters are written in the same place, a machine could be made to recognize those. Also, for real remarkable transformation, even the advanced nonlinear normalization is not sufficient and, for example, for the rotational transformation, the normalization with considerably advanced and considerable computational amount is required. There is a detailed description of this issue in the chapter 3 of S. Mori, H. Nishida, H. Yamada, “Optical Character Recognition”, Wiley.
By the way, for example, it is possible to take a curvature, as the feature of the above. From this viewpoint, in “one of improvements of the correlative method for character recognition”, IEICE Transaction, J-62, 3, PP. 217-224], especially because “g” and “y” cursive style and the Arabic numeral “9” are easily mistaken in the handwriting, these are named as the rotational feature, and the tangential angle differences of curved portions are determined and are suitably quantized and the character is represented by the directional feature vector of related art and the local rotational feature vector and all-feature vector that summarizes them is determined and gradational processing is performed (this is actually performed on the grid plane). And, a method in which a gradational all-feature standard vector is determined in each category and the similarity calculation is performed and answer is obtained has been proposed. By the way, in the standpoint of structure analysis, the “g” and “y” cursive style and the Arabic numeral “9” are fairly different. It is because the structure of upper portion is seen to the positive. However, in feature matching, because those are mixed in the process of the inner product and are made to be one scalar quantity, the structure of upper portion is seen to the negative, and because the three characters have a strong straight line structure, these are buried to this straight line. Therefore, a localized feature surface is expressly prepared. However, as will be mentioned later, by means of our method, the above so-called rotational feature is determined in a large area, “is not local”, and is represented naturally easily with a consistent form. Therefore, the above three kinds of characters are able to be recognized very easily.
An immutable recognition system to the rotation is desired to the object in the wide area, for example, a figure, a body placed on the logistics system, or an airplane or the like, in the military technologies.
Accordingly, the research is being continued from the past, and many papers are disclosed at even the present. As for research up until 1990, such is described in detail in Fundamental of Image Recognition (II) by coauthors, S. Mori; T. Itakura, and also, as for research up until 1999, such is described in detail in Optical Character Recognition by coauthors: S. Mori; H. Nishida; H. Yamada. The main focus of those research projects are the moment method in which the moments with high degree are combined so that the phase angles are offset. Also, there is the application of Fourier transform method that is so-called Fourier descriptors, and this research is prospering by reflecting that recent PCs have becomes very high speed. But such applications are not appearing in the market yet for practical use. And, on the other hand, other than those streams, in the “Online Handwritten Figure Recognition System without depending on the number of strokes, stroke order, rotation and delimitation”, Journal of Information Processing Society of Japan, 27-5, May. 1986, the object is approximated by the linear line segment and arc sequences and is represented by the relative angle changes of those and the object (input figure) correspondence to the same representation of the dictionary is taken, and the mutual distances are measured by the sum of the absolute values of the difference of each angle change, and consequently the recognition system is made to be immutable to rotation. And, however, a disadvantage is present in that it is weak to the detection of acute angle, as is described in the paper itself.
A so-called matching method is a feature (for example, a direction of stroke) matching on 2-dimensional plane where a character is placed. Also, the online handwritten character recognition is a DP (Dynamic Programming) matching or Flexible string matching in another name. As for the former, the distribution of features on 2-dimensions is represented with vectors and the distance between characters is defined by the inner product of those vectors. The character recognition is carried out with the statistical technique as the discrimination issue on the vector space that is defined by the inner product. As for the latter, the matching of simple superposition is expanded and the character recognition is carried out by adaptationally and flexibly matching the input character to the standard character.
Other than such methods, generally, the character recognition referred to as structural analytical methods have been studied. Such methods may be applied for general figures and is a good method, but it needs the symbolization of the subject that is the general figure specifically, and consequently the matching is carried out by the symbol. However, there is a problem in this symbolization, and the flexibility is lost by the symbolization and also the design may not be done mechanically, and consequently the research and development has deadlocked. For example, the above “Online real-time recognition for handwritten number/katakana character”, Transactions of Institute of Electronics and Communication Engineers, 56-D, 5, :PP. 312-319, was disclosed in 1973, and therefore is old. At this stage, a clockwise/counterclockwise of the online character was used as the feature, but all these sequences were symbolized. Those sequences were detected by the increase/decrease of X-coordinate value of an input pattern and were represented with eleven symbols. Such symbolic representation is not flexible, and is partially used only in the special case, actually. Subsequently, an algebraic idea that gave a united viewpoint was introduced to the structural analytical method by the “Algebraic structure representation of shapes”, in Transaction of Institute of Electronics and Communication Engineers, J64-D, 8, P705-712, in 1981, and a practical algebraic system was constructed by the “Algebraic Description of Curve Structure”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 14, No. 5, PP. 1029-1058, in 1992, but it was still symbolic representation. The structural analytical method has stayed at the symbolic representation level, like that. In order to break through this barrier, although it has been known in the art that the analysis should focus not on a symbol but it must focus on an analogue, a specific method relating to the analogue has not been found up to now.
On the other hand, the above techniques of the past have basic problems such as following.
The structural analysis is simple, but there is no flexibility and there is a problem at the boundary of the basic pattern and it is discrete and awkward and it is troublesome to need to prepare a dictionary.
The processing of the pattern matching method, specifically the DP method, is heavy.
The pattern matching method only reads it during offline recognition, specifically it is a main purpose that assigns an input character to a dictionary, and the correspondence to the cause/result is not seen and the misreading that is hard to understand is caused sometimes and it is not rare that the cause is not understandable to a designer.